Introduction to Pythonfor Biologists

Lecture 2

This Lecture

Stuart BrownAssociate Professor

NYU School of Medicine

Learning Objectives

• Flow control (if/else) and Operators• For loops• Recursion• Reading and Writing files (File I/O)• Create custom functions with def• Dictionaries

Flow control

• Programs need to make decisions, and have controlled looping (repeat operations for a specific number of times).

Decision operators: if, elif, elseLooping operators:

for x in list: while a < 10:

For Loops• For loops iterate (step) through a list one element at a time. • In Python, loops and decisions are set off by a colon and an

indent. • Python ‘for’ syntax is very simple, but you must use correct

indent of statements in the loop

>>> my_list=['G', 'A', 'hat', 'cat'] >>> concat = "" # this is an empty string >>> for i in my_list:

concat = concat + i >>> print (concat)GAhatcat

Loop through a String

• For loops work on strings as if they were a list of characters.

>>> my_dna ='ATGCGTA'>>> for i in my_DNA:

print (i)ATGC

if/else example

>>> my_DNA = "ATGCGTA“>>> if my_DNA.find("GC"):

print (“GC is found”)else:

print (“No GC found”)

Operators

• Operators include the basic math functions: +, -, /, *, ** (raise to power)

• Comparisons: >, <, >=, <=, ==• Boolean operators: and, or, not

Example

dna=‘GATCCGGTTACTACGACCTGA’count_G=0count_A=0for base in dna:

if base == 'G':count_G += 1

elif base == 'A'count_A += 1

print ('G= ' + str(count_G) + ' ' + 'A= ' + str(count_A)

Functions• More complex operators are also known as functions• They can deal with file I/O, more complex math, or

other manipulations of data.• Functions use parentheses to act on some data

object, and may take additional parametersprint(x)open('filename', r)read(filehandle)my_list.append(42)write(data, 'filename')len(my_dna)

Range• range(start,stop,[step]) creates a list of integers

– Starts at zero by default– A range does not include the stop number– Step is optional

>>> range(10)[0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> range(4, 11, 2) # from 4 to 11 with step of 2 [4, 6, 8, 10]

• range() is often used as part of a for loop to step through a list while keeping track of what number item you are working on:>>> a = ['Mary', 'had', 'a', 'little', 'lamb']>>> for i in range(len(a)): print i, a[i]0 Mary1 had2 a3 little4 lamb

List Compression• A list compression creates a list using a

function and a for loop. An optional if statement can be included.

squares = [] # create a list of squares < 50for x in range(10):

if (x**2) <50: squares.append(x**2)

print squares [0, 1, 4, 9, 16, 25, 36, 49]

# create a list of squares < 50 with a list compression

squares = [x**2 for x in range(10) if (x**2) < 50] print squares [0, 1, 4, 9, 16, 25, 36, 49]

Custom Functions• In Python, users can create their own

functions, which act like subroutines• or use functions within code written by others

(known as modules)

def g_count(dna): #function takes a string as input

count=0for base in dna:

if base == ‘G’:count += 1

return(count) #function returns an integer

ATG finder

>>> def find_ATG(dna):if dna.find("ATG"):

return ("ATG is found")else:

return ("No ATG found")>>> my_dna =‘TATGCGTA‘>>> find_ATG(my_dna)ATG is found

Bonus point if you find and fix some of the bugs in this code

Recursion• Now that you can make custom functions …– what would happen if you wrote a function that

called itself?def countdown(n):

if n <= 0:print “Blastoff!”

else:print ncountdown(n-1)

• Of course, you should avoid creating an infinite loop …def plustwo(n):

print nplustwo(n+2) #be careful running this- get ready to kill it

Fibonacci• Computer Scientists use recursion often, it is

less common in Bioinformatics applications.• has several sections that explore

algorithms in computational biology and beyond. – There is a nice (fairly simple) problem about

Fibonacci Numbers: http://rosalind.info/problems/fibo/

– Give it a try (in Python, of course).

def Fib(x):def Fib(x):

if x =0:return 0

elif x = 1:return 1

elif x > 1:return Fib(x-1) + Fib(x-2)

• Why is this program such a bad idea?• How can you do it better using a simple list to store the Fib series?• This is also a good introduction to computational complexity.

Bioinformatics often deals with large data and complex computations, so the speed of computing for a given task is an important issue.

File I/O

• Usually your programs will get input data in a text file, and you will want to write output to a file rather than dump it on the screen (“standard output”, “stdout”)

• In Python, a file must be opened before reading or writing. The open file is assigned to a variable called a ‘handle’, then the program will read or write to the handle

• The .read() method captures the whole contents of the file in a single string.

• .close() the file when you are done with it.

file1 = open(‘human_pep.fasta’) Hum_pep = file1.read()gene_count = Hum_pep.count(‘>’)file1.close()

with open() as f

• A nicer way to open a file is to use the with/as keywords and an indented block. This automatically closes the file when the indented block is completed.

>>> with open(‘human_pep.fasta’) as file1:Hum_pep = file1.read()

Write output to a file

• To create an output file, open a file (give it any name you want) with the ‘w’ option and assign it to a variable name.

• Then use the write() method. write() works just like print(), you can include string methods, concatenation, etc. inside the parentheses.

output=open('humpep_count.txt', 'w')output.write('Gene Count: ' + str(gene_count))output.close()

Read a file line by line with a for loop

• readlines() captures a file as a list of lines (rather than all in one big string), then you can loop over the list of lines.

my_file = open(‘human_dna.fasta’) human_seq = my_file.readlines()for line in human_seq:

print (len(line))

• Or you can iterate over lines in the file directly with a for loop:my_file = open(‘human_dna.fasta’) for line in my_file:

print (len(line))

Dictionaries

• Dictionaries contain key-value pairs. (Called a “hash” in most other programming languages)

my_dict1 = {'ATT' : 'I', 'CTT' : 'L', 'GTT' : 'V',

'TTT' : 'F'}

• Very useful for lookup lists of things like the amino acid codon table or k-mer lists

• Designed to give very fast random access lookup of the key and return the corresponding value

• Keys must be unique strings, values can be anything

Zip makes a dictionary

• Rather than type a dictionary, you can build a dictionary from two lists using zip()

>>> list1 = ('GAT', 'CAT', 'TAT', 'AAT')>>> list2 = (1, 2, 3, 4)>>> zip(list1,list2)[('GAT', 1), ('CAT', 2), ('TAT', 3), ('AAT', 4)]

Check and add to dictionary• Another useful application of a dictionary is to build a non-redundant

list. – For each item, check if it is in the dictionary, if not then add it to the

dictionary. – You can count occurrences at the same time.

Example: count DNA dimers

DNA = 'GATCCGGTTACTACGACCTGAGAT'Dimers = {} #create an empty dictionaryfor x in range(len(DNA)):

di = DNA[x:(x+2)] if di in Dimers:

Dimers[di] += 1 #add one to count for dielse:

Dimers[di] = 1 #add di to Dimers dictprint Dimers

Bonus point if you find and fix the bugs in this code

Challenge Assignment

• Write a function that translates a DNA string into protein.

• In your function, use a dictionary of triplet codons as keys and amino acids as values

• Begin translation at the first ATG codon • Write a program that uses your translate

function to open and translate a file that contains a single DNA sequence as text, write the output as another text file.

Zip a codon table(save yourself some typing)

codons= ['ttt', 'ttc', 'tta', 'ttg', 'tct', 'tcc', 'tca', 'tcg', 'tat', 'tac', 'taa', 'tag', 'tgt', 'tgc', 'tga', 'tgg', 'ctt', 'ctc', 'cta', 'ctg', 'cct', 'ccc', 'cca', 'ccg', 'cat', 'cac', 'caa', 'cag', 'cgt', 'cgc', 'cga', 'cgg', 'att', 'atc', 'ata', 'atg', 'act', 'acc', 'aca', 'acg', 'aat', 'aac', 'aaa', 'aag', 'agt', 'agc', 'aga', 'agg', 'gtt', 'gtc', 'gta', 'gtg', 'gct', 'gcc', 'gca', 'gcg', 'gat', 'gac', 'gaa', 'gag', 'ggt', 'ggc', 'gga', 'ggg']

amino_acids = 'FFLLSSSSYY**CC*WLLLLPPPPHHQQRRRRIIIMTTTTNNKKSSRRVVVVAAAADDEEGGGG‘

>>> codon_table = dict(zip(codons, amino_acids))

Very nice Python code by Peter Collingridge: http://www.petercollingridge.co.uk/python-bioinformatics-tools/codon-table

Re-use Code vs Write NewA little break for a philosophical debate

• When should you find and re-use code written by others and when should you write your own?

• In Bioinformatics, many of the problems you will encounter with data have been faced by other people. – A great deal of code has been written and shared in public repositories.– Some of this code has been published an cited in the literature– Don’t try to re-write BLAST (unless you really, really have to)

• If you can’t find code to do exactly what you want, should you adapt existing, or write your own?– There are challenges to figuring out someone else’s code– New code that uses (depends) on programs written by others is very fragile– There are challenges to validate your own code when using it to analyze and publish

scientific data– There is value to building your own repository of code elements from scratch that

work and fit together in a way that is intuitive for you

Some Statistics in Python

• NumPy has some basic statistics functions that work on arrays.

>>> squares = [x**2 for x in range(10) if (x**2) < 50]>>> sq=np.array(squares)>>> np.mean(sq)17.5>>> np.median(sq)12.5>>> np.std(sq)16.680827317612277

Other NumPy funcions

NumPy has:• linear algebra• trigonometry• logarithms• polynomials• Fourier Transformations • random sampling• permutations• sorting• and distributions (normal, Poisson, hypergeometrix, logistic,

gamma, negative binomial, etc)

SciPy

• SciPy is an extension of NumPy that provides a great deal more complex mathematic, statistical, and scientific data analysis functions.

>>> import antigravity

Summary

• Flow control (if/else) and Operators• For loops• Recursion• Reading and Writing files (File I/O)• Create custom functions with def• Dictionaries

Next Lecture: Biopython